Overview

Dataset statistics

Number of variables58
Number of observations45222
Missing cells0
Missing cells (%)0.0%
Duplicate rows44
Duplicate rows (%)0.1%
Total size in memory20.0 MiB
Average record size in memory464.0 B

Variable types

Numeric8
Categorical50

Alerts

Dataset has 44 (0.1%) duplicate rowsDuplicates
age is highly overall correlated with marital_status_Never-married and 1 other fieldsHigh correlation
educational_num is highly overall correlated with education_10th and 13 other fieldsHigh correlation
native_country is highly overall correlated with race_Asian-Pac-IslanderHigh correlation
gender is highly overall correlated with relationship_HusbandHigh correlation
education_10th is highly overall correlated with educational_numHigh correlation
education_11th is highly overall correlated with educational_numHigh correlation
education_12th is highly overall correlated with educational_numHigh correlation
education_4th is highly overall correlated with educational_numHigh correlation
education_6th is highly overall correlated with educational_numHigh correlation
education_8th is highly overall correlated with educational_numHigh correlation
education_9th is highly overall correlated with educational_numHigh correlation
education_Assoc-voc is highly overall correlated with educational_numHigh correlation
education_Bachelors is highly overall correlated with educational_numHigh correlation
education_Doctorate is highly overall correlated with educational_numHigh correlation
education_HS-grad is highly overall correlated with educational_numHigh correlation
education_Masters is highly overall correlated with educational_numHigh correlation
education_Prof-school is highly overall correlated with educational_numHigh correlation
marital_status_Married-civ-spouse is highly overall correlated with marital_status_Never-married and 2 other fieldsHigh correlation
marital_status_Never-married is highly overall correlated with age and 2 other fieldsHigh correlation
relationship_Husband is highly overall correlated with gender and 2 other fieldsHigh correlation
relationship_Not-in-family is highly overall correlated with marital_status_Married-civ-spouseHigh correlation
relationship_Own-child is highly overall correlated with ageHigh correlation
occupation_Prof-specialty is highly overall correlated with educational_numHigh correlation
race_Asian-Pac-Islander is highly overall correlated with native_countryHigh correlation
race_Black is highly overall correlated with race_WhiteHigh correlation
race_White is highly overall correlated with race_BlackHigh correlation
education_10th is highly imbalanced (82.1%)Imbalance
education_11th is highly imbalanced (77.7%)Imbalance
education_12th is highly imbalanced (90.1%)Imbalance
education_4th is highly imbalanced (95.5%)Imbalance
education_6th is highly imbalanced (92.0%)Imbalance
education_8th is highly imbalanced (86.9%)Imbalance
education_9th is highly imbalanced (88.8%)Imbalance
education_Assoc-acdm is highly imbalanced (78.9%)Imbalance
education_Assoc-voc is highly imbalanced (74.3%)Imbalance
education_Doctorate is highly imbalanced (90.6%)Imbalance
education_Masters is highly imbalanced (69.0%)Imbalance
education_Preschool is highly imbalanced (98.3%)Imbalance
education_Prof-school is highly imbalanced (87.4%)Imbalance
marital_status_Married-AF-spouse is highly imbalanced (99.2%)Imbalance
marital_status_Married-spouse-absent is highly imbalanced (90.5%)Imbalance
marital_status_Separated is highly imbalanced (80.0%)Imbalance
marital_status_Widowed is highly imbalanced (81.5%)Imbalance
relationship_Other-relative is highly imbalanced (80.6%)Imbalance
relationship_Unmarried is highly imbalanced (51.3%)Imbalance
relationship_Wife is highly imbalanced (73.0%)Imbalance
occupation_Armed-Forces is highly imbalanced (99.6%)Imbalance
occupation_Farming-fishing is highly imbalanced (79.2%)Imbalance
occupation_Handlers-cleaners is highly imbalanced (73.4%)Imbalance
occupation_Machine-op-inspct is highly imbalanced (65.0%)Imbalance
occupation_Other-service is highly imbalanced (51.1%)Imbalance
occupation_Priv-house-serv is highly imbalanced (95.4%)Imbalance
occupation_Protective-serv is highly imbalanced (85.0%)Imbalance
occupation_Tech-support is highly imbalanced (79.9%)Imbalance
occupation_Transport-moving is highly imbalanced (70.8%)Imbalance
race_Amer-Indian-Eskimo is highly imbalanced (92.2%)Imbalance
race_Asian-Pac-Islander is highly imbalanced (81.2%)Imbalance
race_Black is highly imbalanced (55.2%)Imbalance
race_Other is highly imbalanced (93.4%)Imbalance
workclass has 1406 (3.1%) zerosZeros
capital_gain has 41432 (91.6%) zerosZeros
capital_loss has 43082 (95.3%) zerosZeros

Reproduction

Analysis started2024-02-12 07:13:05.679084
Analysis finished2024-02-12 07:14:15.865784
Duration1 minute and 10.19 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.547941
Minimum17
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:16.070767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q128
median37
Q347
95-th percentile62
Maximum90
Range73
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.21787
Coefficient of variation (CV)0.34289432
Kurtosis-0.15587445
Mean38.547941
Median Absolute Deviation (MAD)10
Skewness0.53281589
Sum1743215
Variance174.71209
MonotonicityNot monotonic
2024-02-12T12:44:16.368973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 1283
 
2.8%
33 1279
 
2.8%
31 1274
 
2.8%
35 1272
 
2.8%
23 1241
 
2.7%
34 1234
 
2.7%
37 1229
 
2.7%
30 1215
 
2.7%
38 1211
 
2.7%
28 1198
 
2.6%
Other values (64) 32786
72.5%
ValueCountFrequency (%)
17 493
 
1.1%
18 695
1.5%
19 864
1.9%
20 916
2.0%
21 935
2.1%
22 1034
2.3%
23 1241
2.7%
24 1130
2.5%
25 1133
2.5%
26 1092
2.4%
ValueCountFrequency (%)
90 46
0.1%
89 1
 
< 0.1%
88 5
 
< 0.1%
87 1
 
< 0.1%
86 1
 
< 0.1%
85 5
 
< 0.1%
84 9
 
< 0.1%
83 9
 
< 0.1%
82 10
 
< 0.1%
81 27
0.1%

workclass
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1048605
Minimum0
Maximum7
Zeros1406
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:16.627708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q33
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1445257
Coefficient of variation (CV)0.36862387
Kurtosis1.9373966
Mean3.1048605
Median Absolute Deviation (MAD)0
Skewness0.065654977
Sum140408
Variance1.309939
MonotonicityNot monotonic
2024-02-12T12:44:16.936406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 33307
73.7%
5 3796
 
8.4%
1 3100
 
6.9%
6 1946
 
4.3%
4 1646
 
3.6%
0 1406
 
3.1%
7 21
 
< 0.1%
ValueCountFrequency (%)
0 1406
 
3.1%
1 3100
 
6.9%
3 33307
73.7%
4 1646
 
3.6%
5 3796
 
8.4%
6 1946
 
4.3%
7 21
 
< 0.1%
ValueCountFrequency (%)
7 21
 
< 0.1%
6 1946
 
4.3%
5 3796
 
8.4%
4 1646
 
3.6%
3 33307
73.7%
1 3100
 
6.9%
0 1406
 
3.1%

fnlwgt
Real number (ℝ)

Distinct26741
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189734.73
Minimum13492
Maximum1490400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:19.013846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13492
5-th percentile39927.8
Q1117388.25
median178316
Q3237926
95-th percentile379522
Maximum1490400
Range1476908
Interquartile range (IQR)120537.75

Descriptive statistics

Standard deviation105639.2
Coefficient of variation (CV)0.55677309
Kurtosis6.1843316
Mean189734.73
Median Absolute Deviation (MAD)60537
Skewness1.4475156
Sum8.5801842 × 109
Variance1.115964 × 1010
MonotonicityNot monotonic
2024-02-12T12:44:20.354260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203488 21
 
< 0.1%
120277 18
 
< 0.1%
125892 18
 
< 0.1%
113364 17
 
< 0.1%
126569 17
 
< 0.1%
186934 16
 
< 0.1%
99185 16
 
< 0.1%
111567 16
 
< 0.1%
190290 16
 
< 0.1%
126675 15
 
< 0.1%
Other values (26731) 45052
99.6%
ValueCountFrequency (%)
13492 1
 
< 0.1%
13769 3
< 0.1%
14878 1
 
< 0.1%
18827 1
 
< 0.1%
19214 1
 
< 0.1%
19302 6
< 0.1%
19395 2
 
< 0.1%
19410 2
 
< 0.1%
19447 1
 
< 0.1%
19491 2
 
< 0.1%
ValueCountFrequency (%)
1490400 1
< 0.1%
1484705 1
< 0.1%
1455435 1
< 0.1%
1366120 1
< 0.1%
1268339 1
< 0.1%
1226583 1
< 0.1%
1210504 1
< 0.1%
1184622 1
< 0.1%
1161363 1
< 0.1%
1125613 1
< 0.1%

educational_num
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.11846
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:21.293745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q313
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5528812
Coefficient of variation (CV)0.25229938
Kurtosis0.63512358
Mean10.11846
Median Absolute Deviation (MAD)1
Skewness-0.31062095
Sum457577
Variance6.5172024
MonotonicityNot monotonic
2024-02-12T12:44:21.520142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 14783
32.7%
10 9899
21.9%
13 7570
16.7%
14 2514
 
5.6%
11 1959
 
4.3%
7 1619
 
3.6%
12 1507
 
3.3%
6 1223
 
2.7%
4 823
 
1.8%
15 785
 
1.7%
Other values (6) 2540
 
5.6%
ValueCountFrequency (%)
1 72
 
0.2%
2 222
 
0.5%
3 449
 
1.0%
4 823
 
1.8%
5 676
 
1.5%
6 1223
 
2.7%
7 1619
 
3.6%
8 577
 
1.3%
9 14783
32.7%
10 9899
21.9%
ValueCountFrequency (%)
16 544
 
1.2%
15 785
 
1.7%
14 2514
 
5.6%
13 7570
16.7%
12 1507
 
3.3%
11 1959
 
4.3%
10 9899
21.9%
9 14783
32.7%
8 577
 
1.3%
7 1619
 
3.6%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1
30527 
0
14695 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

Length

2024-02-12T12:44:21.889153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:22.140485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

Most occurring characters

ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30527
67.5%
0 14695
32.5%

capital_gain
Real number (ℝ)

Distinct121
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101.4303
Minimum0
Maximum99999
Zeros41432
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:22.375850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5013
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7506.4301
Coefficient of variation (CV)6.8151655
Kurtosis150.15129
Mean1101.4303
Median Absolute Deviation (MAD)0
Skewness11.789002
Sum49808883
Variance56346493
MonotonicityNot monotonic
2024-02-12T12:44:22.642362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41432
91.6%
15024 498
 
1.1%
7688 391
 
0.9%
7298 351
 
0.8%
99999 229
 
0.5%
3103 146
 
0.3%
5178 137
 
0.3%
5013 116
 
0.3%
4386 102
 
0.2%
3325 81
 
0.2%
Other values (111) 1739
 
3.8%
ValueCountFrequency (%)
0 41432
91.6%
114 8
 
< 0.1%
401 2
 
< 0.1%
594 42
 
0.1%
914 10
 
< 0.1%
991 4
 
< 0.1%
1055 31
 
0.1%
1086 5
 
< 0.1%
1151 13
 
< 0.1%
1173 2
 
< 0.1%
ValueCountFrequency (%)
99999 229
0.5%
41310 3
 
< 0.1%
34095 4
 
< 0.1%
27828 56
 
0.1%
25236 14
 
< 0.1%
25124 4
 
< 0.1%
22040 1
 
< 0.1%
20051 44
 
0.1%
18481 2
 
< 0.1%
15831 8
 
< 0.1%

capital_loss
Real number (ℝ)

Distinct97
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.595418
Minimum0
Maximum4356
Zeros43082
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:23.023338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4356
Range4356
Interquartile range (IQR)0

Descriptive statistics

Standard deviation404.95609
Coefficient of variation (CV)4.5708469
Kurtosis19.363969
Mean88.595418
Median Absolute Deviation (MAD)0
Skewness4.5163042
Sum4006462
Variance163989.44
MonotonicityNot monotonic
2024-02-12T12:44:23.801257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 43082
95.3%
1902 294
 
0.7%
1977 246
 
0.5%
1887 228
 
0.5%
2415 68
 
0.2%
1485 65
 
0.1%
1848 65
 
0.1%
1876 59
 
0.1%
1590 58
 
0.1%
1740 57
 
0.1%
Other values (87) 1000
 
2.2%
ValueCountFrequency (%)
0 43082
95.3%
155 1
 
< 0.1%
213 5
 
< 0.1%
323 5
 
< 0.1%
419 1
 
< 0.1%
625 17
 
< 0.1%
653 4
 
< 0.1%
810 2
 
< 0.1%
880 6
 
< 0.1%
974 2
 
< 0.1%
ValueCountFrequency (%)
4356 1
 
< 0.1%
3900 2
 
< 0.1%
3770 4
 
< 0.1%
3683 2
 
< 0.1%
3175 2
 
< 0.1%
3004 4
 
< 0.1%
2824 12
< 0.1%
2754 2
 
< 0.1%
2603 5
 
< 0.1%
2559 17
< 0.1%

hours_per_week
Real number (ℝ)

Distinct96
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.938017
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:24.279976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q140
median40
Q345
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.007508
Coefficient of variation (CV)0.29330947
Kurtosis3.2014249
Mean40.938017
Median Absolute Deviation (MAD)3
Skewness0.34054514
Sum1851299
Variance144.18025
MonotonicityNot monotonic
2024-02-12T12:44:24.684893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 21358
47.2%
50 4094
 
9.1%
45 2602
 
5.8%
60 2085
 
4.6%
35 1776
 
3.9%
20 1602
 
3.5%
30 1467
 
3.2%
55 1020
 
2.3%
25 820
 
1.8%
48 733
 
1.6%
Other values (86) 7665
 
16.9%
ValueCountFrequency (%)
1 12
 
< 0.1%
2 24
 
0.1%
3 35
 
0.1%
4 47
 
0.1%
5 60
 
0.1%
6 59
 
0.1%
7 32
 
0.1%
8 152
0.3%
9 24
 
0.1%
10 332
0.7%
ValueCountFrequency (%)
99 123
0.3%
98 14
 
< 0.1%
97 2
 
< 0.1%
96 9
 
< 0.1%
95 2
 
< 0.1%
94 1
 
< 0.1%
92 3
 
< 0.1%
91 3
 
< 0.1%
90 41
 
0.1%
89 3
 
< 0.1%

native_country
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.401022
Minimum0
Maximum40
Zeros26
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-02-12T12:44:25.064876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q138
median38
Q338
95-th percentile38
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.0798561
Coefficient of variation (CV)0.16702433
Kurtosis17.7141
Mean36.401022
Median Absolute Deviation (MAD)0
Skewness-4.2175257
Sum1646127
Variance36.964651
MonotonicityNot monotonic
2024-02-12T12:44:25.394995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
38 41292
91.3%
25 903
 
2.0%
29 283
 
0.6%
10 193
 
0.4%
32 175
 
0.4%
1 163
 
0.4%
7 147
 
0.3%
18 147
 
0.3%
4 133
 
0.3%
8 119
 
0.3%
Other values (31) 1667
 
3.7%
ValueCountFrequency (%)
0 26
 
0.1%
1 163
0.4%
2 113
0.2%
3 82
0.2%
4 133
0.3%
5 97
0.2%
6 43
 
0.1%
7 147
0.3%
8 119
0.3%
9 36
 
0.1%
ValueCountFrequency (%)
40 23
 
0.1%
39 83
 
0.2%
38 41292
91.3%
37 26
 
0.1%
36 29
 
0.1%
35 55
 
0.1%
34 101
 
0.2%
33 20
 
< 0.1%
32 175
 
0.4%
31 62
 
0.1%

income
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
34014 
1
11208 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

Length

2024-02-12T12:44:25.702171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:25.906625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34014
75.2%
1 11208
 
24.8%

education_10th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43999 
1
 
1223

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

Length

2024-02-12T12:44:26.082914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:26.282380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43999
97.3%
1 1223
 
2.7%

education_11th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43603 
1
 
1619

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

Length

2024-02-12T12:44:26.451966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:26.657376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43603
96.4%
1 1619
 
3.6%

education_12th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44645 
1
 
577

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

Length

2024-02-12T12:44:26.820937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:27.025042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44645
98.7%
1 577
 
1.3%

education_4th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
45000 
1
 
222

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

Length

2024-02-12T12:44:27.214568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:27.419984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45000
99.5%
1 222
 
0.5%

education_6th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44773 
1
 
449

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

Length

2024-02-12T12:44:27.630418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:27.877756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44773
99.0%
1 449
 
1.0%

education_8th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44399 
1
 
823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

Length

2024-02-12T12:44:28.087197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:28.471168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44399
98.2%
1 823
 
1.8%

education_9th
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44546 
1
 
676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%

Length

2024-02-12T12:44:28.744437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:28.958867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44546
98.5%
1 676
 
1.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43715 
1
 
1507

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

Length

2024-02-12T12:44:29.157335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:29.381736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43715
96.7%
1 1507
 
3.3%

education_Assoc-voc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43263 
1
 
1959

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%

Length

2024-02-12T12:44:29.559257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:29.787645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43263
95.7%
1 1959
 
4.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
37652 
1
7570 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

Length

2024-02-12T12:44:29.992102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:30.195555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37652
83.3%
1 7570
 
16.7%

education_Doctorate
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44678 
1
 
544

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%

Length

2024-02-12T12:44:30.371088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:30.560577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44678
98.8%
1 544
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
30439 
1
14783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

Length

2024-02-12T12:44:30.729127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:30.953526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

Most occurring characters

ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30439
67.3%
1 14783
32.7%

education_Masters
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
42708 
1
 
2514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%

Length

2024-02-12T12:44:31.103226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:31.274183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42708
94.4%
1 2514
 
5.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
45150 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

Length

2024-02-12T12:44:31.416223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:31.587066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45150
99.8%
1 72
 
0.2%

education_Prof-school
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44437 
1
 
785

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%

Length

2024-02-12T12:44:31.733684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:31.924036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44437
98.3%
1 785
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
35323 
1
9899 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%

Length

2024-02-12T12:44:32.114061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:32.326496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35323
78.1%
1 9899
 
21.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
38925 
1
6297 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%

Length

2024-02-12T12:44:32.569841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:32.873033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38925
86.1%
1 6297
 
13.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
45190 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%

Length

2024-02-12T12:44:33.281936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:33.631999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45190
99.9%
1 32
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
24167 
1
21055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%

Length

2024-02-12T12:44:34.124680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:34.359053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%

Most occurring characters

ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24167
53.4%
1 21055
46.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44670 
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%

Length

2024-02-12T12:44:34.520625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:34.727111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44670
98.8%
1 552
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
30624 
1
14598 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%

Length

2024-02-12T12:44:34.918561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:35.102103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%

Most occurring characters

ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30624
67.7%
1 14598
32.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43811 
1
 
1411

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%

Length

2024-02-12T12:44:35.296924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:35.474871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43811
96.9%
1 1411
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43945 
1
 
1277

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%

Length

2024-02-12T12:44:35.677328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:35.878640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43945
97.2%
1 1277
 
2.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
26556 
1
18666 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%

Length

2024-02-12T12:44:36.015166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:36.180795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%

Most occurring characters

ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26556
58.7%
1 18666
41.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
33520 
1
11702 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%

Length

2024-02-12T12:44:36.323311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:36.491137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33520
74.1%
1 11702
 
25.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43873 
1
 
1349

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%

Length

2024-02-12T12:44:36.630224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:36.796131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43873
97.0%
1 1349
 
3.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
38596 
1
6626 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%

Length

2024-02-12T12:44:36.934016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:37.107276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38596
85.3%
1 6626
 
14.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
40434 
1
4788 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%

Length

2024-02-12T12:44:37.283804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:37.474154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40434
89.4%
1 4788
 
10.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43131 
1
 
2091

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%

Length

2024-02-12T12:44:37.612878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:37.778539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43131
95.4%
1 2091
 
4.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
39682 
1
5540 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%

Length

2024-02-12T12:44:37.914172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:38.096991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39682
87.7%
1 5540
 
12.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
45208 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%

Length

2024-02-12T12:44:38.247651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:38.428853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45208
> 99.9%
1 14
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
39202 
1
6020 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%

Length

2024-02-12T12:44:38.564791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:38.730369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39202
86.7%
1 6020
 
13.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
39238 
1
5984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%

Length

2024-02-12T12:44:38.873756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:39.040292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39238
86.8%
1 5984
 
13.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43742 
1
 
1480

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%

Length

2024-02-12T12:44:39.180438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:39.346741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43742
96.7%
1 1480
 
3.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43176 
1
 
2046

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%

Length

2024-02-12T12:44:39.483504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:39.649417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43176
95.5%
1 2046
 
4.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
42252 
1
 
2970

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%

Length

2024-02-12T12:44:39.785081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:39.953171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42252
93.4%
1 2970
 
6.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
40414 
1
4808 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%

Length

2024-02-12T12:44:40.089229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:40.253065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%

Most occurring characters

ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40414
89.4%
1 4808
 
10.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44990 
1
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%

Length

2024-02-12T12:44:40.396101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:40.561900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44990
99.5%
1 232
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
39214 
1
6008 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%

Length

2024-02-12T12:44:40.698687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:40.866683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%

Most occurring characters

ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39214
86.7%
1 6008
 
13.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44246 
1
 
976

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

Length

2024-02-12T12:44:41.008779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:41.173989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44246
97.8%
1 976
 
2.2%

occupation_Sales
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
39814 
1
5408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%

Length

2024-02-12T12:44:41.310039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:41.478503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%

Most occurring characters

ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39814
88.0%
1 5408
 
12.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43802 
1
 
1420

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%

Length

2024-02-12T12:44:41.617088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:41.783340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43802
96.9%
1 1420
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
42906 
1
 
2316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%

Length

2024-02-12T12:44:41.919533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:42.085903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42906
94.9%
1 2316
 
5.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44787 
1
 
435

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

Length

2024-02-12T12:44:42.223966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:42.388181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44787
99.0%
1 435
 
1.0%

race_Asian-Pac-Islander
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43919 
1
 
1303

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

Length

2024-02-12T12:44:42.525672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:42.689170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43919
97.1%
1 1303
 
2.9%

race_Black
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
40994 
1
4228 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

Length

2024-02-12T12:44:42.823903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:42.992790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40994
90.7%
1 4228
 
9.3%

race_Other
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
44869 
1
 
353

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

Length

2024-02-12T12:44:43.138399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:43.302686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44869
99.2%
1 353
 
0.8%

race_White
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1
38903 
0
6319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45222
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Length

2024-02-12T12:44:43.437591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-12T12:44:43.604507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Most occurring characters

ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45222
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38903
86.0%
0 6319
 
14.0%

Interactions

2024-02-12T12:44:10.553095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:54.111271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:56.410939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:58.695821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:01.366675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:03.908874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:06.255611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:08.479684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:10.811406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:54.425248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:56.706148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:59.023943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:01.647922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:04.218047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:06.510912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:08.744939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:11.077694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:54.695529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:56.977421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:59.303200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:02.012945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:04.549159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:06.788169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:09.027179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:11.359937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:55.018660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:57.257674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:59.598410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:02.402902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:04.963053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:07.074441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:09.286485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:11.644176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:55.342793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:57.517973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:00.247669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:02.687146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:05.254276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:07.330775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:09.543797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:12.111966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:55.606092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:57.861055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:00.542880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:02.944454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:05.498630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:07.592019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:09.789140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:12.342308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:55.858418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:58.149283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:00.803182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:03.246645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:05.741972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:07.893212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:10.034483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:12.596628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:56.109741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:43:58.401614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:01.080444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:03.571778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:05.992299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:08.187426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-02-12T12:44:10.272845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-02-12T12:44:44.037158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ageworkclassfnlwgteducational_numcapital_gaincapital_losshours_per_weeknative_countrygenderincomeeducation_10theducation_11theducation_12theducation_4theducation_6theducation_8theducation_9theducation_Assoc-acdmeducation_Assoc-voceducation_Bachelorseducation_Doctorateeducation_HS-gradeducation_Masterseducation_Preschooleducation_Prof-schooleducation_Some-collegemarital_status_Divorcedmarital_status_Married-AF-spousemarital_status_Married-civ-spousemarital_status_Married-spouse-absentmarital_status_Never-marriedmarital_status_Separatedmarital_status_Widowedrelationship_Husbandrelationship_Not-in-familyrelationship_Other-relativerelationship_Own-childrelationship_Unmarriedrelationship_Wifeoccupation_Adm-clericaloccupation_Armed-Forcesoccupation_Craft-repairoccupation_Exec-managerialoccupation_Farming-fishingoccupation_Handlers-cleanersoccupation_Machine-op-inspctoccupation_Other-serviceoccupation_Priv-house-servoccupation_Prof-specialtyoccupation_Protective-servoccupation_Salesoccupation_Tech-supportoccupation_Transport-movingrace_Amer-Indian-Eskimorace_Asian-Pac-Islanderrace_Blackrace_Otherrace_White
age1.0000.073-0.0770.0670.1210.0610.1570.0060.1210.3120.0660.1340.0750.0360.0340.1350.0330.0510.0530.1090.0740.0420.1270.0090.0730.1320.1990.0180.3730.0230.6040.0640.3170.3580.0980.0950.5330.0960.0700.0580.0040.0860.1310.0620.1140.0260.1540.0580.0990.0250.0760.0390.0480.0170.0180.0220.0310.039
workclass0.0731.000-0.0390.0110.0230.0080.067-0.0020.1430.1630.0380.0600.0280.0240.0320.0490.0290.0270.0050.0730.1040.0860.1550.0070.1240.0360.0470.0060.1710.0120.1540.0230.0290.1740.0680.0470.1110.0630.0350.1670.0980.1060.1410.2430.0960.1310.0790.0410.2110.2580.1550.0620.0400.0430.0270.1010.0210.100
fnlwgt-0.077-0.0391.000-0.032-0.010-0.002-0.021-0.0810.0270.0120.0170.0000.0140.0330.0350.0000.0070.0150.0170.0070.0000.0160.0130.0080.0020.0150.0130.0000.0240.0220.0370.0210.0280.0170.0150.0220.0200.0100.0180.0070.0010.0000.0120.0370.0250.0180.0080.0000.0100.0180.0000.0100.0000.0570.0700.1120.0000.063
educational_num0.0670.011-0.0321.0000.1200.0780.1650.0750.0680.3570.6440.7441.0000.8680.5880.8001.0000.3701.0000.8950.6340.6361.0000.4930.7640.4830.0630.0000.1310.0520.1170.0520.0660.1250.0780.0870.1820.0690.0430.1410.0050.1750.2290.1270.1370.1600.1890.0910.5180.0480.0950.1040.1290.0340.0780.0840.0670.052
capital_gain0.1210.023-0.0100.1201.000-0.0670.0930.0200.0490.2690.0240.0240.0110.0030.0100.0170.0130.0090.0040.0610.0810.0650.0710.0670.1870.0430.0310.0000.0800.0060.0620.0110.0000.0740.0270.0270.0550.0370.0180.0390.0000.0300.0870.0320.0280.0360.0490.0030.0940.0150.0100.0070.0210.0000.0140.0200.0040.019
capital_loss0.0610.008-0.0020.078-0.0671.0000.0600.0120.0650.1970.0110.0190.0110.0000.0000.0280.0160.0020.0000.0430.0480.0350.0560.0000.0600.0220.0590.0000.1240.0140.0800.0360.0440.1140.0610.0190.0580.0820.0250.0290.0000.0170.0580.0150.0260.0120.0430.0460.0520.0000.0040.0040.0000.0000.0000.0250.0070.024
hours_per_week0.1570.067-0.0210.1650.0930.0601.0000.0210.2410.2660.0590.1320.0610.0130.0340.0330.0240.0140.0380.1110.0710.0940.0860.0200.0980.0830.0670.0100.2390.0170.2350.0440.0960.2720.0420.0620.3070.0820.0630.1320.0000.1060.1640.1350.0710.1020.2170.0770.0750.0460.1100.0400.0740.0180.0300.1080.0230.116
native_country0.006-0.002-0.0810.0750.0200.0120.0211.0000.0420.0750.0190.0150.0240.2270.3140.0760.0840.0210.0260.0550.0620.0590.0580.1250.0470.0550.0500.0000.0250.1050.0230.0230.0000.0270.0270.1250.0390.0250.0280.0370.0000.0110.0540.0790.0550.0610.0650.0850.0680.0140.0390.0180.0240.0140.5340.1270.1680.266
gender0.1210.1430.0270.0680.0490.0650.2410.0421.0000.2160.0000.0060.0000.0090.0170.0240.0120.0240.0070.0160.0290.0140.0000.0000.0470.0570.2370.0170.4380.0400.1790.1070.1770.5820.1730.0470.1030.3220.3170.2780.0100.2280.0300.1020.0930.0320.1650.0940.0340.0630.0230.0240.1340.0110.0000.1150.0030.103
income0.3120.1630.0120.3570.2690.1970.2660.0750.2161.0000.0690.0860.0450.0340.0460.0570.0540.0050.0000.1780.1240.1360.1720.0200.1560.0570.1340.0100.4460.0380.3190.0740.0600.4040.1950.0850.2230.1470.1210.0960.0000.0200.2090.0560.0910.0760.1650.0380.1830.0220.0170.0170.0220.0280.0130.0900.0240.083
education_10th0.0660.0380.0170.6440.0240.0110.0590.0190.0000.0691.0000.0310.0180.0100.0150.0220.0190.0300.0350.0740.0170.1160.0400.0020.0210.0880.0050.0000.0220.0030.0120.0200.0210.0170.0150.0120.0300.0150.0110.0370.0000.0270.0480.0220.0340.0370.0650.0000.0600.0120.0100.0250.0390.0080.0170.0220.0000.012
education_11th0.1340.0600.0000.7440.0240.0190.1320.0150.0060.0860.0311.0000.0210.0120.0180.0250.0230.0350.0400.0860.0200.1340.0460.0040.0250.1020.0180.0000.0590.0000.0700.0130.0000.0520.0320.0210.1100.0050.0220.0350.0000.0170.0570.0080.0580.0220.0750.0150.0630.0130.0120.0280.0270.0080.0160.0230.0060.016
education_12th0.0750.0280.0141.0000.0110.0110.0610.0240.0000.0450.0180.0211.0000.0050.0090.0140.0120.0200.0230.0500.0110.0790.0270.0000.0140.0600.0110.0000.0340.0000.0470.0000.0000.0340.0170.0170.0680.0000.0080.0120.0000.0050.0330.0100.0260.0170.0390.0120.0370.0000.0000.0150.0200.0000.0050.0170.0200.016
education_4th0.0360.0240.0330.8680.0030.0000.0130.2270.0090.0340.0100.0120.0051.0000.0030.0070.0060.0110.0130.0310.0040.0480.0160.0000.0070.0360.0130.0000.0040.0340.0150.0000.0270.0020.0000.0290.0190.0000.0030.0200.0000.0000.0220.0450.0210.0260.0290.0550.0230.0050.0180.0110.0000.0000.0000.0000.0350.008
education_6th0.0340.0320.0350.5880.0100.0000.0340.3140.0170.0460.0150.0180.0090.0031.0000.0120.0100.0170.0200.0440.0090.0690.0230.0000.0120.0530.0210.0000.0090.0400.0120.0190.0000.0070.0110.0600.0300.0060.0000.0310.0000.0020.0350.0460.0400.0510.0330.0500.0370.0120.0250.0150.0130.0000.0150.0020.0400.011
education_8th0.1350.0490.0000.8000.0170.0280.0330.0760.0240.0570.0220.0250.0140.0070.0121.0000.0150.0240.0280.0610.0130.0950.0320.0000.0170.0720.0140.0000.0310.0100.0440.0000.0510.0360.0190.0140.0280.0000.0090.0400.0000.0270.0390.0720.0200.0490.0280.0280.0470.0050.0290.0180.0330.0000.0090.0000.0240.000
education_9th0.0330.0290.0071.0000.0130.0160.0240.0840.0120.0540.0190.0230.0120.0060.0100.0151.0000.0220.0250.0550.0120.0860.0290.0000.0150.0650.0000.0000.0000.0030.0070.0100.0080.0000.0140.0210.0000.0100.0000.0340.0000.0260.0360.0210.0360.0410.0390.0300.0460.0040.0190.0180.0200.0000.0100.0200.0190.016
education_Assoc-acdm0.0510.0270.0150.3700.0090.0020.0140.0210.0240.0050.0300.0350.0200.0110.0170.0240.0221.0000.0390.0830.0190.1290.0450.0040.0240.0980.0210.0000.0080.0000.0000.0000.0080.0150.0170.0140.0140.0130.0160.0350.0000.0110.0130.0160.0210.0230.0190.0080.0000.0140.0080.0470.0230.0000.0000.0000.0000.000
education_Assoc-voc0.0530.0050.0171.0000.0040.0000.0380.0260.0070.0000.0350.0400.0230.0130.0200.0280.0250.0391.0000.0950.0220.1480.0510.0050.0270.1120.0230.0000.0120.0060.0270.0000.0000.0100.0000.0150.0270.0170.0050.0070.0000.0340.0070.0120.0230.0150.0180.0050.0000.0170.0240.0740.0220.0080.0000.0080.0050.008
education_Bachelors0.1090.0730.0070.8950.0610.0430.1110.0550.0160.1780.0740.0860.0500.0310.0440.0610.0550.0830.0951.0000.0490.3120.1090.0170.0590.2370.0420.0000.0450.0040.0120.0360.0390.0390.0540.0350.0630.0600.0180.0310.0000.1190.1700.0450.0750.0980.1080.0220.2040.0040.0620.0330.0820.0260.0470.0500.0130.030
education_Doctorate0.0740.1040.0000.6340.0810.0480.0710.0620.0290.1240.0170.0200.0110.0040.0090.0130.0120.0190.0220.0491.0000.0770.0260.0000.0130.0580.0110.0000.0450.0000.0370.0040.0000.0480.0000.0140.0390.0160.0000.0380.0000.0400.0040.0180.0230.0280.0370.0000.2100.0130.0300.0110.0230.0030.0290.0260.0000.010
education_HS-grad0.0420.0860.0160.6360.0650.0350.0940.0590.0140.1360.1160.1340.0790.0480.0690.0950.0860.1290.1480.3120.0771.0000.1690.0270.0920.3690.0330.0000.0000.0000.0420.0260.0330.0000.0250.0290.0060.0370.0020.0310.0000.1270.1080.0210.0600.1030.0490.0040.2260.0000.0310.0530.0970.0120.0400.0370.0110.012
education_Masters0.1270.1550.0131.0000.0710.0560.0860.0580.0000.1720.0400.0460.0270.0160.0230.0320.0290.0450.0510.1090.0260.1691.0000.0070.0320.1280.0000.0000.0510.0050.0430.0190.0080.0440.0290.0320.0780.0190.0220.0600.0000.0850.1220.0360.0500.0590.0730.0160.2630.0220.0290.0110.0500.0110.0310.0350.0080.019
education_Preschool0.0090.0070.0080.4930.0670.0000.0200.1250.0000.0200.0020.0040.0000.0000.0000.0000.0000.0040.0050.0170.0000.0270.0071.0000.0000.0200.0110.0000.0090.0120.0150.0000.0000.0110.0180.0000.0010.0000.0000.0080.0000.0020.0120.0440.0000.0140.0230.0070.0120.0000.0090.0030.0000.0000.0140.0020.0040.014
education_Prof-school0.0730.1240.0020.7640.1870.0600.0980.0470.0470.1560.0210.0250.0140.0070.0120.0170.0150.0240.0270.0590.0130.0920.0320.0001.0000.0700.0180.0000.0650.0080.0440.0140.0110.0680.0110.0150.0470.0280.0000.0430.0000.0470.0200.0170.0280.0350.0410.0070.2720.0170.0370.0130.0280.0070.0290.0310.0000.014
education_Some-college0.1320.0360.0150.4830.0430.0220.0830.0550.0570.0570.0880.1020.0600.0360.0530.0720.0650.0980.1120.2370.0580.3690.1280.0200.0701.0000.0170.0000.0690.0080.0700.0000.0190.0640.0000.0090.0960.0030.0090.1010.0000.0120.0020.0210.0130.0350.0160.0180.1080.0320.0490.0340.0250.0080.0120.0110.0000.002
marital_status_Divorced0.1990.0470.0130.0630.0310.0590.0670.0500.2370.1340.0050.0180.0110.0130.0210.0140.0000.0210.0230.0420.0110.0330.0000.0110.0180.0171.0000.0080.3750.0440.2780.0720.0680.3370.2630.0060.0890.3320.0880.0800.0020.0310.0080.0410.0260.0000.0160.0090.0080.0040.0180.0120.0200.0110.0310.0150.0050.000
marital_status_Married-AF-spouse0.0180.0060.0000.0000.0000.0000.0100.0000.0170.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0081.0000.0240.0000.0170.0000.0000.0000.0140.0000.0060.0060.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_Married-civ-spouse0.3730.1710.0240.1310.0800.1240.2390.0250.4380.4460.0220.0590.0340.0040.0090.0310.0000.0080.0120.0450.0450.0000.0510.0090.0650.0690.3750.0241.0000.1030.6440.1670.1590.8970.5500.1150.3710.3210.2320.1500.0000.1260.0980.0440.0520.0090.1710.0500.0380.0370.0080.0150.0780.0180.0080.1230.0110.107
marital_status_Married-spouse-absent0.0230.0120.0220.0520.0060.0140.0170.1050.0400.0380.0030.0000.0000.0340.0400.0100.0030.0000.0060.0040.0000.0000.0050.0120.0080.0080.0440.0000.1031.0000.0760.0190.0180.0930.0640.0320.0120.0720.0240.0080.0000.0000.0120.0160.0030.0000.0180.0150.0070.0040.0070.0090.0000.0100.0390.0170.0250.044
marital_status_Never-married0.6040.1540.0370.1170.0620.0800.2350.0230.1790.3190.0120.0700.0470.0150.0120.0440.0070.0000.0270.0120.0370.0420.0430.0150.0440.0700.2780.0170.6440.0761.0000.1240.1170.5790.3150.1070.4980.0490.1520.0780.0060.0920.0970.0130.0820.0180.1290.0100.0190.0250.0290.0090.0580.0010.0080.0720.0070.068
marital_status_Separated0.0640.0230.0210.0520.0110.0360.0440.0230.1070.0740.0200.0130.0000.0000.0190.0000.0100.0000.0000.0360.0040.0260.0190.0000.0140.0000.0720.0000.1670.0190.1241.0000.0300.1500.0640.0240.0270.1930.0390.0170.0000.0110.0230.0160.0000.0150.0490.0230.0170.0040.0070.0000.0080.0000.0110.0990.0130.082
marital_status_Widowed0.3170.0290.0280.0660.0000.0440.0960.0000.1770.0600.0210.0000.0000.0270.0000.0510.0080.0080.0000.0390.0000.0330.0080.0000.0110.0190.0680.0000.1590.0180.1170.0301.0000.1430.1080.0150.0630.1630.0370.0380.0000.0390.0040.0000.0200.0000.0500.0610.0140.0140.0000.0000.0150.0000.0000.0180.0000.014
relationship_Husband0.3580.1740.0170.1250.0740.1140.2720.0270.5820.4040.0170.0520.0340.0020.0070.0360.0000.0150.0100.0390.0480.0000.0440.0110.0680.0640.3370.0000.8970.0930.5790.1500.1431.0000.4950.1470.3470.2880.1840.1880.0000.1580.0930.0580.0400.0120.1800.0580.0170.0480.0000.0160.0960.0220.0000.1250.0160.118
relationship_Not-in-family0.0980.0680.0150.0780.0270.0610.0420.0270.1730.1950.0150.0320.0170.0000.0110.0190.0140.0170.0000.0540.0000.0250.0290.0180.0110.0000.2630.0140.5500.0640.3150.0640.1080.4951.0000.1030.2450.2030.1300.0430.0000.0540.0000.0320.0180.0110.0300.0210.0450.0100.0100.0200.0320.0000.0170.0000.0000.006
relationship_Other-relative0.0950.0470.0220.0870.0270.0190.0620.1250.0470.0850.0120.0210.0170.0290.0600.0140.0210.0140.0150.0350.0140.0290.0320.0000.0150.0090.0060.0000.1150.0320.1070.0240.0150.1470.1031.0000.0720.0600.0380.0150.0060.0120.0400.0030.0460.0140.0510.0370.0400.0050.0000.0020.0170.0070.0460.0410.0400.070
relationship_Own-child0.5330.1110.0200.1820.0550.0580.3070.0390.1030.2230.0300.1100.0680.0190.0300.0280.0000.0140.0270.0630.0390.0060.0780.0010.0470.0960.0890.0060.3710.0120.4980.0270.0630.3470.2450.0721.0000.1420.0910.0610.0000.0550.0980.0000.0950.0080.1180.0000.0770.0120.0470.0090.0310.0000.0050.0230.0030.020
relationship_Unmarried0.0960.0630.0100.0690.0370.0820.0820.0250.3220.1470.0150.0050.0000.0000.0060.0000.0100.0130.0170.0600.0160.0370.0190.0000.0280.0030.3320.0060.3210.0720.0490.1930.1630.2880.2030.0600.1421.0000.0750.1030.0000.0560.0240.0260.0210.0030.0710.0350.0190.0230.0210.0090.0340.0290.0080.1470.0110.130
relationship_Wife0.0700.0350.0180.0430.0180.0250.0630.0280.3170.1210.0110.0220.0080.0030.0000.0090.0000.0160.0050.0180.0000.0020.0220.0000.0000.0090.0880.0670.2320.0240.1520.0390.0370.1840.1300.0380.0910.0751.0000.0830.0000.0720.0180.0290.0330.0080.0110.0050.0560.0250.0180.0000.0390.0030.0130.0000.0000.009
occupation_Adm-clerical0.0580.1670.0070.1410.0390.0290.1320.0370.2780.0960.0370.0350.0120.0200.0310.0400.0340.0350.0070.0310.0380.0310.0600.0080.0430.1010.0800.0000.1500.0080.0780.0170.0380.1880.0430.0150.0610.1030.0831.0000.0000.1460.1460.0680.0810.0990.1290.0260.1460.0550.1380.0670.0870.0000.0110.0480.0000.045
occupation_Armed-Forces0.0040.0980.0010.0050.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0060.0000.0000.0000.0000.0060.0000.0000.0000.0001.0000.0020.0020.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.000
occupation_Craft-repair0.0860.1060.0000.1750.0300.0170.1060.0110.2280.0200.0270.0170.0050.0000.0020.0270.0260.0110.0340.1190.0400.1270.0850.0020.0470.0120.0310.0000.1260.0000.0920.0110.0390.1580.0540.0120.0550.0560.0720.1460.0021.0000.1530.0720.0850.1040.1350.0270.1530.0580.1440.0700.0910.0000.0250.0450.0010.049
occupation_Exec-managerial0.1310.1410.0120.2290.0870.0580.1640.0540.0300.2090.0480.0570.0330.0220.0350.0390.0360.0130.0070.1700.0040.1080.1220.0120.0200.0020.0080.0000.0980.0120.0970.0230.0040.0930.0000.0400.0980.0240.0180.1460.0020.1531.0000.0710.0850.1030.1350.0270.1530.0580.1440.0700.0900.0040.0000.0500.0190.048
occupation_Farming-fishing0.0620.2430.0370.1270.0320.0150.1350.0790.1020.0560.0220.0080.0100.0450.0460.0720.0210.0160.0120.0450.0180.0210.0360.0440.0170.0210.0410.0000.0440.0160.0130.0160.0000.0580.0320.0030.0000.0260.0290.0680.0000.0720.0711.0000.0390.0480.0630.0110.0720.0260.0670.0320.0420.0000.0140.0350.0000.036
occupation_Handlers-cleaners0.1140.0960.0250.1370.0280.0260.0710.0550.0930.0910.0340.0580.0260.0210.0400.0200.0360.0210.0230.0750.0230.0600.0500.0000.0280.0130.0260.0000.0520.0030.0820.0000.0200.0400.0180.0460.0950.0210.0330.0810.0000.0850.0850.0391.0000.0570.0750.0140.0850.0320.0800.0390.0500.0140.0120.0210.0000.016
occupation_Machine-op-inspct0.0260.1310.0180.1600.0360.0120.1020.0610.0320.0760.0370.0220.0170.0260.0510.0490.0410.0230.0150.0980.0280.1030.0590.0140.0350.0350.0000.0000.0090.0000.0180.0150.0000.0120.0110.0140.0080.0030.0080.0990.0000.1040.1030.0480.0571.0000.0910.0180.1040.0390.0970.0470.0610.0000.0050.0440.0220.039
occupation_Other-service0.1540.0790.0080.1890.0490.0430.2170.0650.1650.1650.0650.0750.0390.0290.0330.0280.0390.0190.0180.1080.0370.0490.0730.0230.0410.0160.0160.0000.1710.0180.1290.0490.0500.1800.0300.0510.1180.0710.0110.1290.0000.1350.1350.0630.0750.0911.0000.0240.1350.0510.1270.0620.0800.0090.0110.0860.0070.084
occupation_Priv-house-serv0.0580.0410.0000.0910.0030.0460.0770.0850.0940.0380.0000.0150.0120.0550.0500.0280.0300.0080.0050.0220.0000.0040.0160.0070.0070.0180.0090.0000.0500.0150.0100.0230.0610.0580.0210.0370.0000.0350.0050.0260.0000.0270.0270.0110.0140.0180.0241.0000.0270.0080.0260.0110.0150.0000.0000.0290.0040.023
occupation_Prof-specialty0.0990.2110.0100.5180.0940.0520.0750.0680.0340.1830.0600.0630.0370.0230.0370.0470.0460.0000.0000.2040.2100.2260.2630.0120.2720.1080.0080.0000.0380.0070.0190.0170.0140.0170.0450.0400.0770.0190.0560.1460.0020.1530.1530.0720.0850.1040.1350.0271.0000.0580.1440.0700.0910.0040.0380.0470.0000.023
occupation_Protective-serv0.0250.2580.0180.0480.0150.0000.0460.0140.0630.0220.0120.0130.0000.0050.0120.0050.0040.0140.0170.0040.0130.0000.0220.0000.0170.0320.0040.0000.0370.0040.0250.0040.0140.0480.0100.0050.0120.0230.0250.0550.0000.0580.0580.0260.0320.0390.0510.0080.0581.0000.0540.0260.0340.0010.0050.0270.0000.021
occupation_Sales0.0760.1550.0000.0950.0100.0040.1100.0390.0230.0170.0100.0120.0000.0180.0250.0290.0190.0080.0240.0620.0300.0310.0290.0090.0370.0490.0180.0000.0080.0070.0290.0070.0000.0000.0100.0000.0470.0210.0180.1380.0000.1440.1440.0670.0800.0970.1270.0260.1440.0541.0000.0660.0850.0100.0000.0370.0040.037
occupation_Tech-support0.0390.0620.0100.1040.0070.0040.0400.0180.0240.0170.0250.0280.0150.0110.0150.0180.0180.0470.0740.0330.0110.0530.0110.0030.0130.0340.0120.0000.0150.0090.0090.0000.0000.0160.0200.0020.0090.0090.0000.0670.0000.0700.0700.0320.0390.0470.0620.0110.0700.0260.0661.0000.0410.0050.0110.0000.0000.000
occupation_Transport-moving0.0480.0400.0000.1290.0210.0000.0740.0240.1340.0220.0390.0270.0200.0000.0130.0330.0200.0230.0220.0820.0230.0970.0500.0000.0280.0250.0200.0000.0780.0000.0580.0080.0150.0960.0320.0170.0310.0340.0390.0870.0000.0910.0900.0420.0500.0610.0800.0150.0910.0340.0850.0411.0000.0030.0210.0140.0000.000
race_Amer-Indian-Eskimo0.0170.0430.0570.0340.0000.0000.0180.0140.0110.0280.0080.0080.0000.0000.0000.0000.0000.0000.0080.0260.0030.0120.0110.0000.0070.0080.0110.0000.0180.0100.0010.0000.0000.0220.0000.0070.0000.0290.0030.0000.0000.0000.0040.0000.0140.0000.0090.0000.0040.0010.0100.0050.0031.0000.0160.0310.0060.244
race_Asian-Pac-Islander0.0180.0270.0700.0780.0140.0000.0300.5340.0000.0130.0170.0160.0050.0000.0150.0090.0100.0000.0000.0470.0290.0400.0310.0140.0290.0120.0310.0000.0080.0390.0080.0110.0000.0000.0170.0460.0050.0080.0130.0110.0000.0250.0000.0140.0120.0050.0110.0000.0380.0050.0000.0110.0210.0161.0000.0550.0140.427
race_Black0.0220.1010.1120.0840.0200.0250.1080.1270.1150.0900.0220.0230.0170.0000.0020.0000.0200.0000.0080.0500.0260.0370.0350.0020.0310.0110.0150.0000.1230.0170.0720.0990.0180.1250.0000.0410.0230.1470.0000.0480.0000.0450.0500.0350.0210.0440.0860.0290.0470.0270.0370.0000.0140.0310.0551.0000.0280.797
race_Other0.0310.0210.0000.0670.0040.0070.0230.1680.0030.0240.0000.0060.0200.0350.0400.0240.0190.0000.0050.0130.0000.0110.0080.0040.0000.0000.0050.0000.0110.0250.0070.0130.0000.0160.0000.0400.0030.0110.0000.0000.0000.0010.0190.0000.0000.0220.0070.0040.0000.0000.0040.0000.0000.0060.0140.0281.0000.220
race_White0.0390.1000.0630.0520.0190.0240.1160.2660.1030.0830.0120.0160.0160.0080.0110.0000.0160.0000.0080.0300.0100.0120.0190.0140.0140.0020.0000.0000.1070.0440.0680.0820.0140.1180.0060.0700.0200.1300.0090.0450.0000.0490.0480.0360.0160.0390.0840.0230.0230.0210.0370.0000.0000.2440.4270.7970.2201.000

Missing values

2024-02-12T12:44:13.307765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-12T12:44:14.539429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageworkclassfnlwgteducational_numgendercapital_gaincapital_losshours_per_weeknative_countryincomeeducation_10theducation_11theducation_12theducation_4theducation_6theducation_8theducation_9theducation_Assoc-acdmeducation_Assoc-voceducation_Bachelorseducation_Doctorateeducation_HS-gradeducation_Masterseducation_Preschooleducation_Prof-schooleducation_Some-collegemarital_status_Divorcedmarital_status_Married-AF-spousemarital_status_Married-civ-spousemarital_status_Married-spouse-absentmarital_status_Never-marriedmarital_status_Separatedmarital_status_Widowedrelationship_Husbandrelationship_Not-in-familyrelationship_Other-relativerelationship_Own-childrelationship_Unmarriedrelationship_Wifeoccupation_Adm-clericaloccupation_Armed-Forcesoccupation_Craft-repairoccupation_Exec-managerialoccupation_Farming-fishingoccupation_Handlers-cleanersoccupation_Machine-op-inspctoccupation_Other-serviceoccupation_Priv-house-servoccupation_Prof-specialtyoccupation_Protective-servoccupation_Salesoccupation_Tech-supportoccupation_Transport-movingrace_Amer-Indian-Eskimorace_Asian-Pac-Islanderrace_Blackrace_Otherrace_White
0253226802710040380010000000000000000001000001000000001000000000100
138389814910050380000000000001000000100001000000000100000000000001
22813369511210040381000000010000000000100001000000000000000100000001
34431603231017688040381000000000000000100100001000000000001000000000100
4343198693610030380100000000000000000001000100000000000100000000001
56351046261513103032381000000000000001000100001000000000000001000000001
62433696671000040380000000000000000100001000000100000000100000000001
7553104996410010380000001000000000000100001000000010000000000000001
8653184454916418040381000000000001000000100001000000000001000000000001
93602124651310040380000000000100000000100001000001000000000000000001
ageworkclassfnlwgteducational_numgendercapital_gaincapital_losshours_per_weeknative_countryincomeeducation_10theducation_11theducation_12theducation_4theducation_6theducation_8theducation_9theducation_Assoc-acdmeducation_Assoc-voceducation_Bachelorseducation_Doctorateeducation_HS-gradeducation_Masterseducation_Preschooleducation_Prof-schooleducation_Some-collegemarital_status_Divorcedmarital_status_Married-AF-spousemarital_status_Married-civ-spousemarital_status_Married-spouse-absentmarital_status_Never-marriedmarital_status_Separatedmarital_status_Widowedrelationship_Husbandrelationship_Not-in-familyrelationship_Other-relativerelationship_Own-childrelationship_Unmarriedrelationship_Wifeoccupation_Adm-clericaloccupation_Armed-Forcesoccupation_Craft-repairoccupation_Exec-managerialoccupation_Farming-fishingoccupation_Handlers-cleanersoccupation_Machine-op-inspctoccupation_Other-serviceoccupation_Priv-house-servoccupation_Prof-specialtyoccupation_Protective-servoccupation_Salesoccupation_Tech-supportoccupation_Transport-movingrace_Amer-Indian-Eskimorace_Asian-Pac-Islanderrace_Blackrace_Otherrace_White
4521232334066610040380100000000000000000100001000000000010000000010000
45213433846611110045380000000001000000000100001000000000000000010000001
452143231161381410011350000000000000100000001000100000000000000001001000
452155333218651410040381000000000000100000100001000000001000000000000001
452162233101521010040380000000000000000100001000100000000000000100000001
452172732573021200038380000000010000000000100000000010000000000001000001
45218403154374910040381000000000001000000100001000000000001000000000001
45219583151910900040380000000000001000000000010000101000000000000000001
45220223201490910020380000000000001000000001000001001000000000000000001
452215242879279015024040381000000000001000000100000000010001000000000000001

Duplicate rows

Most frequently occurring

ageworkclassfnlwgteducational_numgendercapital_gaincapital_losshours_per_weeknative_countryincomeeducation_10theducation_11theducation_12theducation_4theducation_6theducation_8theducation_9theducation_Assoc-acdmeducation_Assoc-voceducation_Bachelorseducation_Doctorateeducation_HS-gradeducation_Masterseducation_Preschooleducation_Prof-schooleducation_Some-collegemarital_status_Divorcedmarital_status_Married-AF-spousemarital_status_Married-civ-spousemarital_status_Married-spouse-absentmarital_status_Never-marriedmarital_status_Separatedmarital_status_Widowedrelationship_Husbandrelationship_Not-in-familyrelationship_Other-relativerelationship_Own-childrelationship_Unmarriedrelationship_Wifeoccupation_Adm-clericaloccupation_Armed-Forcesoccupation_Craft-repairoccupation_Exec-managerialoccupation_Farming-fishingoccupation_Handlers-cleanersoccupation_Machine-op-inspctoccupation_Other-serviceoccupation_Priv-house-servoccupation_Prof-specialtyoccupation_Protective-servoccupation_Salesoccupation_Tech-supportoccupation_Transport-movingrace_Amer-Indian-Eskimorace_Asian-Pac-Islanderrace_Blackrace_Otherrace_White# duplicates
102132433681100502500000000000000100000010001000000001000000000000013
202531959942000401200001000000000000000010001000000000000100000000013
2125330814413100402500000000001000000000010001000000100000000000000013
01731530218000203800010000000000000000010000010000000000000100000012
11843780368100103800010000000000000000010000010000001000000000000012
2193972619100403800000000000010000000010001000000001000000000000012
31931304313100362500000100000000000000010001000000001000000000000012
419313815310000103800000000000000001000010000010010000000000000000012
519313946610000253800000000000000001000010000010000000000000100000012
619314667910100303800000000000000001000010000010000010000000000001002